Prestroke Disability Predicts Adverse Poststroke Outcome

Han, Thang S.; Fry, Christopher; Gulli, Giosuè; Affley, Brendan; Robin, Jonathan; Irvin-Sellers, Melanie; Fluck, David; Kakar, Puneet; Sharma, Sapna; Sharma, Pankaj

doi:10.1161/strokeaha.119.027740

Cited by 48 publications

(16 citation statements)

References 28 publications

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“…Appropriate adjustments were made including age, sex and presenting diagnoses. Characteristics of this study have been shown to be similar to those of the UK population [12,33,34]. There are certain limitations in the present study that may arise from patients who have left our catchment area or those who were readmitted to other centres this would lead to an underestimation of Fig.…”

Section: Discussionmentioning

confidence: 71%

Early emergency readmission frequency as an indicator of short-, medium- and long-term mortality post-discharge from hospital

et al. 2020

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Frequent emergency readmissions, an indicator of quality of care, has been rising in England but the underlying reasons remain unclear. We examined the association of early readmissions with subsequent mortality in adults, taking into account the underlying presenting diagnoses and hospital length of stay (LOS). Data of alive-discharge episodes were prospectively collected between 01/04/2017 and 31/03/2019 in an National Health Service hospital, comprising 32,270 patients (46.1% men) aged 18–107 years (mean = 64.0, ± SD = 20.5 years). The associations of readmission frequency within 28 days of discharge and mortality within 30 days and 6 months of hospital discharge, and over a 2-year period were evaluated, adjusted for presenting diagnoses, LOS, age and sex during the first admission. Analysis of all patients 18–107 years (reference: no readmission) showed mortality within 30 days was increased for 1 readmission: event rate = 9.2%, odds ratio (OR) = 3.4 (95% confidence interval (CI) = 2.9–4.0), and ≥ 2 readmissions: event rate = 10.0%, OR = 2.6 (95%CI = 2.0–3.3), and within 6 months for 1 readmission: event rate = 19.6%, OR = 3.0 (95%CI = 2.7–3.4), and ≥ 2 readmissions: event rate = 27.4%, OR = 3.4 (95%CI = 2.9–4.0), and over a 2-year period for 1 readmission: event rate = 25.5%, hazard ratio = 2.2 (95%CI = 2.0–2.4), and ≥ 2 readmissions: event rate = 36.1%, hazard ratio = 2.5 (95%CI = 2.2–2.8). Within the age groups 18–49, 50–59, 60–69, 70–79 and ≥ 80 years, readmissions were also associated with increased risk of mortality within 3 months and 6 months of discharge, and over 2-year period. In conclusion, early hospital readmission predicts short-, medium- and long-term mortality post-discharge from hospital in adults aged 18–107 years, independent of underlying presenting conditions, LOS, age and sex. Further research focussing on safe discharge and follow-up patient care may help reduce preventable readmissions and post-discharge mortality.

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Section: Discussionmentioning

confidence: 71%

Early emergency readmission frequency as an indicator of short-, medium- and long-term mortality post-discharge from hospital

et al. 2020

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“…Furthermore, as strokes tend to reoccur in the same patient, even in cases of acute stroke, the chronic lesion segmentation models can aid in foreseeing the prognosis. Chronic stroke segmentation can predict the pre-stroke modified rankin score (pre-mRS) [30], which is an independent predictor of poststroke prognosis [31]. This highlights the role of chronic stroke segmentation models in both acute and chronic stroke settings.…”

Section: Limitationsmentioning

confidence: 89%

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR images

Verma¹,

Kumar²,

Paydarfar³

2022

Preprint

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Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61- 0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97(p-value &lt; 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

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“…Furthermore, as strokes tend to reoccur in the same patient, even in cases of acute stroke, the chronic lesion segmentation models can aid in foreseeing the prognosis. Chronic stroke segmentation can predict the pre-stroke modified Rankin score (pre-mRS) [ 34 ], which is an independent predictor of poststroke prognosis [ 35 ]. This highlights the role of chronic stroke segmentation models in both acute and chronic stroke settings.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images

2022

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Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation, including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61–0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12 mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97 (p-value < 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

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Prestroke Disability Predicts Adverse Poststroke Outcome

Cited by 48 publications

References 28 publications

Early emergency readmission frequency as an indicator of short-, medium- and long-term mortality post-discharge from hospital

Early emergency readmission frequency as an indicator of short-, medium- and long-term mortality post-discharge from hospital

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR images

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images

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